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Recent data gathered and triggered by the SWIFT satellite have greatly improved our knowledge of long-duration gamma ray bursts (GRBs) and X-ray flashes (XRFs). This is particularly the case for the X-ray data at all times. We show that the…

Astrophysics · Physics 2009-09-29 Shlomo Dado , Arnon Dar , Alvaro De Rujula

The immense power of gamma-ray bursts (GRBs) make them ideal probes of the early universe. By using absorption lines in the afterglows of high-redshift GRBs, astronomers can study the evolution of metals in the early universe. With an…

The remarkable practical success of deep learning has revealed some major surprises from a theoretical perspective. In particular, simple gradient methods easily find near-optimal solutions to non-convex optimization problems, and despite…

Statistics Theory · Mathematics 2021-03-17 Peter L. Bartlett , Andrea Montanari , Alexander Rakhlin

Gamma-ray Bursts (GRBs) are highly energetic events that can be observed at extremely high redshift. However, inherent bias in GRB data due to selection effects and redshift evolution can significantly skew any subsequent analysis. We…

High Energy Astrophysical Phenomena · Physics 2021-11-23 Maria Dainotti , Delina Levine , Nissim Fraija , Poonam Chandra

The variability of gamma-ray burst (GRB) is thought to be correlated with its absolute peak luminosity, and this relation had been used to derive an estimate of the redshifts of GRBs. Recently Amati et al. presented the results of spectral…

Astrophysics · Physics 2009-11-07 D. M. Wei , W. H. Gao

We studied some statistical properties of the spatial point process displayed by GRBs of known redshift. To find ring like point patterns we developed an algorithm and defined parameters to characterize the level of compactness and…

Cosmology and Nongalactic Astrophysics · Physics 2017-11-29 Lajos G. Balázs , Lídia Rejtő , Gábor Tusnády

Gamma-ray bursts (GRBs) are tremendous explosions visible across most of the Universe, certainly out to redshifts of z=4.5 and likely out to z~10. Recently, GRBs have been found to have a roughly constant explosive energy as well as to have…

Astrophysics · Physics 2009-11-07 Bradley E. Schaefer

Thanks to their enormous energy release which allows to detect them up to very high redshift, Gamma Rays Bursts (GRBs) have recently attracted a lot of interest to probe the Hubble diagram (HD) deep into the matter dominated era and hence…

Cosmology and Nongalactic Astrophysics · Physics 2015-05-28 V. F. Cardone , M. Perillo , S. Capozziello

Four years after the launch the Swift satellite the nature of the GRBs broadband afterglow behaviour is still an open issue. The standard external shock fireball model cannot easily explain the combined temporal and spectral properties of…

High Energy Astrophysical Phenomena · Physics 2011-05-06 M. Nardini , G. Ghisellini , G. Ghirlanda , A. Celotti

Different forms of long gamma-ray bursts (GRBs) Luminosity Functions are considered on the basis of an explicit physical model. The inferred flux distributions are compared with the observed ones from two samples of GRBs, Swift and Fermi…

High Energy Astrophysical Phenomena · Physics 2021-11-17 Shreya Banerjee , David Eichler , Dafne Guetta

I have used a sample of long Gamma Ray Bursts (GRBs) common to both \emph{Swift} and \emph{Fermi} to re-derive the parameters of the Yonetoku correlation. This allowed me to self-consistently estimate pseudo redshifts of all the bursts with…

High Energy Astrophysical Phenomena · Physics 2017-11-29 Debdutta Paul

We exploit the 14 gamma-ray bursts (GRBs) with known redshifts z and the 7 GRBs for which there are constraints on z to determine the GRB rate R_{GRB}(z), using a method based on Bayesian inference. We find that, despite the qualitative…

Astrophysics · Physics 2009-11-07 Nevin Weinberg , Carlo Graziani , Donald Q. Lamb , Daniel E. Reichart

Gamma-ray bursts (GRBs) are challenging to identify due to their transient nature, complex temporal profiles, and limited observational datasets. We address this with a one-dimensional convolutional neural network integrated with an…

We present a theoretical analysis of Gaussian-binary restricted Boltzmann machines (GRBMs) from the perspective of density models. The key aspect of this analysis is to show that GRBMs can be formulated as a constrained mixture of…

Neural and Evolutionary Computing · Computer Science 2017-02-06 Nan Wang , Jan Melchior , Laurenz Wiskott

Long duration Gamma-Ray Bursts (GRBs) have been strongly connected with core collapse supernovae, so it was surprising when the recent GRB060614 (with a reported redshift of 0.125) was found to have no visible supernova to deep limits.…

Astrophysics · Physics 2007-05-23 Bradley E. Schaefer , Limin Xiao

The widely claimed replicability crisis in science may lead to revised standards of significance. The customary frequentist confidence intervals, calibrated through hypothetical repetitions of the experiment that is supposed to have…

Statistics Theory · Mathematics 2020-02-11 Luigi Pace , Alessandra Salvan

Most current assessments use ex post proxies that miss uncertainty and fail to consistently capture the rapid change in bitcoin mining. We introduce a unified, ex ante statistical model that derives expected return, downside risk, and…

Computational Engineering, Finance, and Science · Computer Science 2025-12-24 Yuting Cai , Ruthav Sadali , Korok Ray , Chao Tian

The cosmological concordance model is consistent with all available observational data, including the apparent distance and redshift relationship for distant supernovae, but it is curious how the Milne cosmological model is able to make…

General Physics · Physics 2007-05-23 Alasdair Macleod

Using high-quality, broad-band afterglow data for GRB 091029, we test the validity of the forward-shock model for gamma-ray burst afterglows. We used multi-wavelength (NIR to X-ray) follow-up observations obtained with the GROND,…

To improve accuracy and speed of regressions and classifications, we present a data-based prediction method, Random Bits Regression (RBR). This method first generates a large number of random binary intermediate/derived features based on…

Machine Learning · Statistics 2016-11-04 Yi Wang , Yi Li , Momiao Xiong , Li Jin